resources.Rmd

# Resources

## Tools

### Test Driven Data Cleaning
Please refer to your respective project repository's readme on how test driven data cleaning can be used.

#### Python users
* [Github Repository](https://github.com/DataKind-SG/test-driven-data-cleaning)  

#### R users  
* [testthat on CRAN](https://github.com/DataKind-SG/test-driven-data-cleaning)  
* [Getting started with testing](https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf)  

### Github
#### Github Desktop and other IDEs
* [Github Desktop User Guides](https://help.github.com/desktop/guides/)  
* [Using version control with RStudio](https://support.rstudio.com/hc/en-us/articles/200532077-Version-Control-with-Git-and-SVN)  

#### Command Line  
* [git - the simple guide](https://rogerdudler.github.io/git-guide/)  

### Trello
* [Trello Tutorial](https://trello.com/b/I7TjiplA/trello-tutorial)  
* [Getting Started with Trello (Video Demo)](https://www.youtube.com/watch?v=xky48zyL9iA)  

## Cheatsheets
You can refer to the below links for cheatsheets that might be useful.

### R
* [Collection of RStudio Cheatsheets](https://www.rstudio.com/resources/cheatsheets/)

### Python
* [Pandas Cheatsheet](https://github.com/pandas-dev/pandas/tree/master/doc/cheatsheet)
* [Python Cheatsheets](https://www.pythonsheets.com/)

## Videos
* [DataLearn: Docker for Reproducible Research, presented by Paul, recorded by Engineers.SG](https://engineers.sg/video/datalearn-docker-for-reproducible-research-datakind-sg--1468)